Abstract
Brain-machines - also termed neural prostheses, could potentially increase substantially the quality of life for people suffering from motor disorders or even brain palsy. In this paper we investigate the non-stationary continuous decoding problem associated to the rat’s hand position. To this aim, intracortical data (also named ECoG for electrocorticogram) are processed in successive stages: spike detection, spike sorting, and intention extraction from the firing rate signal.
The two important questions to answer in our experiment are (i) is it realistic to link time events from the primary motor cortex with some time-delay mapping tool and are some inputs more suitable for this mapping (ii) shall we consider separated channels or a special representation based on multidimensional statistics. We propose our own answers to these questions and demonstrate that a nonlinear representation might be appropriate in a number of situations.
This project was supported in part by funding from the Hubert Curien program of the Foreign French Minister and from the Taiwan NSC. The neural activity recordings were kindly provided by the Neuroengineering lab. of the National Chiao-Tung University.
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Kifouche, A., Vigneron, V., Shamsollahi, M.B., Guessoum, A. (2014). Decoding Hand Trajectory from Primary Motor Cortex ECoG Using Time Delay Neural Network. In: Mladenov, V., Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2014. Communications in Computer and Information Science, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-319-11071-4_23
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DOI: https://doi.org/10.1007/978-3-319-11071-4_23
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